Datasets:

GUIAgent
/

Magic-RICH

@@ -21,69 +21,31 @@ paper: https://arxiv.org/abs/2508.03700
 repository: https://huggingface.co/datasets/GUIAgent/Magic-RICH
 ---
-# Dataset Card for Magic-RICH
-## Dataset Summary
-**Magic-RICH** is a Chinese benchmark dataset for evaluating **mobile GUI agents** in realistic smartphone environments.
-It contains **4,000 step-level samples** across **four subsets**, covering 17 categories and over 150 popular apps.
-Unlike many previous GUI datasets, Magic-RICH also includes **special actions** such as screenshot and long screenshot to better reflect real-world interactions.
-This dataset is designed for **evaluation only** (no train/dev split) and was used in the development of **MagicGUI**, an open-source GUI agent built on Qwen2-VL.
----
-## Subsets
-Magic-RICH is composed of **four balanced subsets** (1,000 samples each):
-- **Routine**: High-frequency, single-step actions (e.g., tap, scroll, text input).
-- **Instruction**: Direct user commands (e.g., "Open...", "Check membership"), testing instruction-to-action mapping.
-- **Complex**: Harder tasks requiring reasoning (logical conditions, visual analysis, multi-step navigation).
-- **Handling Exception**: Special cases including
   - *Non-interactive* (page cannot be acted on),
   - *Completed* (task already finished),
-  - *Loading* (page still in transition).
----
-## Dataset Structure
-Each sample contains:
-- `image`: current mobile screen (screenshot)
-- `query`: user instruction or question (Chinese)
-- `label`: ground truth **primitive action** (e.g., `tap(x,y)`, `scroll(x,y,dir)`)
-- `meta`: metadata including subset category and app class
-Split:
-- **test**: 4,000 samples total (no training/dev data)
----
-## Evaluation Protocol
 Three metrics are recommended for evaluation:
-- **Type** – action type accuracy (e.g., Tap vs. Scroll)
-- **Grd** – grounding accuracy (tap/scroll location falls inside ground-truth element box)
-- **SR (Step Success Rate)** – full correctness at the step level (all parameters correct)
----
-## Intended Use
-- Benchmarking mobile GUI agents in **step-level action prediction, grounding, and task completion**.
-- Applicable to research in **multimodal reasoning, planning, and robust agent execution**.
----
-## Limitations
-- Dataset is **evaluation-only** (no train/val).
-- Focused on **Chinese ecosystem apps**; transferability to non-Chinese environments may require additional datasets.
----
-## Ethical Considerations
-- Data collected using **pre-registered accounts** on real devices, separated from real users.
-- Measures taken to avoid **personally identifiable information (PII)** and sensitive data.
----
-## Citation
 If you use Magic-RICH in your research, please cite:
 ```bibtex

 repository: https://huggingface.co/datasets/GUIAgent/Magic-RICH
 ---
+# Dataset Summary
+**Magic-RICH** is a Chinese benchmark dataset for evaluating **mobile GUI agents** in realistic smartphone environments. It contains **4,000 step-level samples** across **four subsets**, covering 17 categories and over 150 popular apps. Unlike many previous GUI datasets, Magic-RICH also includes **special actions** such as screenshot and long screenshot to better reflect real-world interactions. This dataset is designed for **evaluation only** (no train/dev split) and was used in the development of **MagicGUI**, an open-source GUI agent.
+# Subsets
+Magic-RICH is composed of four balanced subsets (1,000 samples each):
+- Routine: High-frequency, single-step actions (e.g., tap, scroll, text input).
+- Instruction: Direct user commands (e.g., "Open...", "Check membership"), testing instruction-to-action mapping.
+- Complex: Harder tasks requiring reasoning (logical conditions, visual analysis, multi-step navigation).
+- Handling Exception: Special cases including
   - *Non-interactive* (page cannot be acted on),
   - *Completed* (task already finished),
+  - *Loading* (page still in transition).
+# Evaluation Protocol
 Three metrics are recommended for evaluation:
+- Type – action type accuracy (e.g., Tap vs. Scroll)
+- Grd – grounding accuracy (tap/scroll location falls inside ground-truth element box)
+- SR – full correctness at the step level (all parameters correct)
+# License
+* This project is licensed under the [Apache-2.0](./LICENSE) license. The model weights are fully open for academic research, and commercial use licenses can be applied for by contacting magicgui@honor.com. This project uses the pre-trained Qwen2VL-7B-Instruct for initialization, which is also licensed under the Apache- 2.0 License.
+# Citation
 If you use Magic-RICH in your research, please cite:
 ```bibtex